Journal Title：Data Science And Engineering

The journal of Data Science and Engineering (DSE) responds to the remarkable change in the focus of information technology development from CPU-intensive computation to data-intensive computation, where the effective application of data, especially big data, becomes vital. The emerging discipline data science and engineering, an interdisciplinary field integrating theories and methods from computer science, statistics, information science, and other fields, focuses on the foundations and engineering of efficient and effective techniques and systems for data collection and management, for data integration and correlation, for information and knowledge extraction from massive data sets, and for data use in different application domains. Focusing on the theoretical background and advanced engineering approaches, DSE aims to offer a prime forum for researchers, professionals, and industrial practitioners to share their knowledge in this rapidly growing area.

It provides in-depth coverage of the latest advances in the closely related fields of data science and data engineering. More specifically, DSE covers four areas: (i) the data itself, i.e., the nature and quality of the data, especially big data; (ii) the principles of information extraction from data, especially big data; (iii) the theory behind data-intensive computing; and (iv) the techniques and systems used to analyze and manage big data. DSE welcomes papers that explore the above subjects. Specific topics include, but are not limited to: (a) the nature and quality of data, (b) the computational complexity of data-intensive computing,(c) new methods for the design and analysis of the algorithms for solving problems with big data input,(d) collection and integration of data collected from internet and sensing devises or sensor networks, (e) representation, modeling, and visualization of  big data,(f)  storage, transmission, and management of big data,(g) methods and algorithms of  data intensive computing, such asmining big data,online analysis processing of big data,big data-based machine learning, big data based decision-making, statistical computation of big data, graph-theoretic computation of big data, linear algebraic computation of big data, and  big data-based optimization. (h) hardware systems and software systems for data-intensive computing, (i) data security, privacy, and trust, and(j) novel applications of big data.

《數(shù)據(jù)科學(xué)與工程》（DSE）雜志響應(yīng)了信息技術(shù)發(fā)展重點(diǎn)從 CPU 密集型計算到數(shù)據(jù)密集型計算的顯著變化，其中數(shù)據(jù)（尤其是大數(shù)據(jù)）的有效應(yīng)用變得至關(guān)重要。新興學(xué)科數(shù)據(jù)科學(xué)與工程是一門跨學(xué)科領(lǐng)域，整合了計算機(jī)科學(xué)、統(tǒng)計學(xué)、信息科學(xué)和其他領(lǐng)域的理論和方法，專注于數(shù)據(jù)收集和管理、數(shù)據(jù)集成和關(guān)聯(lián)、從海量數(shù)據(jù)集中提取信息和知識以及在不同應(yīng)用領(lǐng)域使用數(shù)據(jù)的高效技術(shù)和系統(tǒng)的基礎(chǔ)和工程。DSE 專注于理論背景和先進(jìn)的工程方法，旨在為研究人員、專業(yè)人士和行業(yè)從業(yè)者提供一個主要論壇，分享他們在這個快速增長領(lǐng)域的知識。

它深入報道了數(shù)據(jù)科學(xué)和數(shù)據(jù)工程密切相關(guān)領(lǐng)域的最新進(jìn)展。更具體地說，DSE 涵蓋四個領(lǐng)域：（i）數(shù)據(jù)本身，即數(shù)據(jù)（尤其是大數(shù)據(jù)）的性質(zhì)和質(zhì)量；（ii）從數(shù)據(jù)（尤其是大數(shù)據(jù)）中提取信息的原理； (iii) 數(shù)據(jù)密集型計算背后的理論；(iv) 用于分析和管理大數(shù)據(jù)的技術(shù)和系統(tǒng)。DSE 歡迎探討上述主題的論文。具體主題包括但不限于：(a) 數(shù)據(jù)的性質(zhì)和質(zhì)量；(b) 數(shù)據(jù)密集型計算的計算復(fù)雜性；(c) 用于解決大數(shù)據(jù)輸入問題的算法的設(shè)計和分析的新方法；(d) 從互聯(lián)網(wǎng)和傳感設(shè)備或傳感器網(wǎng)絡(luò)收集的數(shù)據(jù)的收集和集成；(e) 大數(shù)據(jù)的表示、建模和可視化；(f) 大數(shù)據(jù)的存儲、傳輸和管理；(g) 數(shù)據(jù)密集型計算的方法和算法，如大數(shù)據(jù)挖掘、大數(shù)據(jù)在線分析處理、基于大數(shù)據(jù)的機(jī)器學(xué)習(xí)、基于大數(shù)據(jù)的決策、大數(shù)據(jù)統(tǒng)計計算、大數(shù)據(jù)圖論計算、大數(shù)據(jù)線性代數(shù)計算以及基于大數(shù)據(jù)的優(yōu)化。 (h) 數(shù)據(jù)密集型計算的硬件系統(tǒng)和軟件系統(tǒng)，(i) 數(shù)據(jù)安全、隱私和信任，以及(j) 大數(shù)據(jù)的新應(yīng)用。

Data Science And Engineering由Springer Nature出版商出版，收稿方向涵蓋Engineering - Computational Mechanics全領(lǐng)域，此刊是該細(xì)分領(lǐng)域中屬于非常不錯的SCI期刊，在行業(yè)細(xì)分領(lǐng)域中學(xué)術(shù)影響力較大，專業(yè)度認(rèn)可很高，所以對原創(chuàng)文章要求創(chuàng)新性較高，如果您的文章質(zhì)量很高，可以嘗試。平均審稿速度 12 Weeks ，影響因子指數(shù)5.1，該期刊近期沒有被列入國際期刊預(yù)警名單，廣大學(xué)者值得一試。